Feng He

The paper proposes a comprehensive framework for LLM-based agent unlearning, enabling agents to selectively forget specific knowledge (states, trajectories, or environments) while maintaining performance and resisting knowledge inference by adversaries.

The paper proposes ADAM, a novel and highly effective privacy attack that systematically extracts sensitive data from LLM agent memory by adaptively querying the victim agent's memory based on data distribution and entropy.

LISA is a novel LLM-based invariant testing framework for software functional bugs, achieving higher bug-detection rates and competitive code coverage than fuzzing and prior LLM-based test generation approaches.